The invention relates to base station equipment for receiving and transmitting one or more signals from one or more users, in which the signals may arrive at the equipment along a multiplicity of paths, from possibly different directions, and with possibly different delays.
A major concern for providers of wireless communications services is system coverage and capacity. Future systems promise data rates and an aggregate capacity significantly higher than current systems. However, with conventional base stations, the maximum link closure range will be decreased substantially for users operating at higher data rates. As a result, the promised data rates and aggregate capacity can only be supported in a small region close to the base station.
Smart antenna systems have been discussed in the literature as a means of increasing capacity and coverage over and above that which can be provided with simple omni-directional antennas. They achieve this through spatio-temporal correlation of desired signals and co-channel interference within a cell. Interference suppression is implemented by forming narrow radiation patterns, forming radiation nulls on significant interference points or a combination of the two. Smart antennas are implemented in several forms; switched-beam, Direction-of-Arrival (DOA) or Optimum Combining derived adaptive-beams. Some systems are analog where the beam is formed in an RF manifold such as a Butler matrix but the most flexible are those that are digitally formed.
Switched beam systems such as the one described in U.S. Pat. No. 6,218,987 entitled “Radio Antenna System”, form several fixed beams in an RF Butler matrix with the ability to simultaneously broadcast a common channel with a high gain wide beam. A similar fixed beam system is described in U.S. Pat. No. 6,181,276 entitled “Sector Shaping Transition System and Method” where a combination of a set of fixed beams can be coherently combined, by analog means, to form another beam that is better adapted to the area loading of the cell. These switched-beam systems do not take advantage of the maximum gain offered by the full aperture. As a mobile moves through a cell it will suffer beam-width modulation as it travels between the peaks of the several fixed beams. Furthermore, switched-beam approaches simply further sub-divide a cell into sub-sectors. Unfortunately, this method requires handoff between the sub-sectors just as with a standard 3-sector system. These handovers require valuable resources and ultimately reduce the capacity of the network.
In the recent papers entitled “A comparison of Tracking-Beam arrays and Switching-Beam Arrays Operating in a CDMA Mobile Communication Channer”, IEEE Antennas and Propagation Magazine, Dec. 1999, and “Smart Antennas”, IEEE Antennas and Propagation Magazine, Jun. 2000, “Smart Antennas for Mobile Communication systems: Benefits and Challenges”, Electronics and Communication Engineering Journal, Apr. 1999, it has been shown that adaptive-beam systems perform better than switched-beam systems especially in high interference environments. They perform better partly because they take spatial and temporal correlations of interfering signals into account and eliminate the need for frequent handovers within a sector and also tend to maintain maximum antenna gain in the desired direction
Adaptive-beam systems can be used to track individual mobile terminals within a base station service area. Several different methods are disclosed in the literature that all attempt to find an array weighting vector that maximizes the SINR for a desired signal. These methods vary in complexity. Estimation of signal parameters by rotational invariance techniques (ESPRIT) is more widely used than the other sub-space eigen-decomposition method Multiple Signal Classification (MUSIC). Although MUSIC is considered to achieve higher resolution it also requires more computation in its searching algorithm than the closed form solution provided by ESPRIT. These eigen-decomposition methods require a good estimate of the array covariance matrix by averaging over time, such that in the limit where the averaging time approaches infinity the estimate becomes exact. The array covariance matrix is found by averaging over several snapshots of the array signal values. Once determined the matrix can be updated after every sample. Several recent patent disclosures cite the use of ESPRIT for adaptive beam-forming such as U.S. Pat. No. 6,008,759 entitled “Method of Determining the Direction of Arrival of a radio Signal, as well as Radio Base Station and Radio Communications Systems” and U.S. Pat. No. 5,892,700 “Method for the High Resolution Evaluation of Signals for One or Two Dimensional Directional or Frequency Estimation”. In the preferred embodiment of the former, a sub-optimal method is introduced that forms a beam based on the steering vector determined from the strongest eigen-value. This eliminates the need for full eigen-decomposition and significantly reduces computation time allowing faster track updates. It is considered sub-optimal because it does not attempt to place nulls on significant interference and thus does not maximizing SINR. However, it is suggested that interference may be suppressed further by standard side-lobe control methods. Furthermore, ubiquitous multi-path propagation with uncorrelated fading would require at least two paths to be resolved requiring means for several channels. In one embodiment of the latter, ESPRIT is used to resolve several multi-path signals from a single desired source simultaneously and therefore take advantage of maximal ratio combining. Although this technique is considered optimum combining because it maximizes SINR it has the disadvantage that its solution does not necessarily place the peak of an antenna beam on the desired signal path. The effect of this is the degradation in sensitivity of the system to the thermal noise thus reducing the range of the base station.
The methods described in the above-mentioned references require computationally expensive eigen-decomposition of the estimated array covariance matrix requiring at least an (M×M) matrix inversion where M is the number of antenna elements. Accordingly as the number of users, K, and the number of elements, M, grows the matrix manipulation will become unwieldy and memory intensive. Conversely, the method and apparatus of this invention replaces the (M×M) matrix inversion to a single computation of a ratio. Furthermore, the above-mentioned references describe methods that incorporate switched and fixed beam solutions that carry the burden of frequent handovers.
Other less computationally intensive methods for adaptive beam-forming and direction finding do exist. For example, a simpler means of DOA estimation disclosed in U.S. Pat. No. 6,212,406, “Method for Providing Angular Diversity, and Base Station Equipment”, outlines a search and track by scan method, relying on beam-width modulation, determines directions and delays of signals by seeking the strongest power levels or largest SINR. In a multi-path environment where the signal could jump discontinuously, too much time could elapse before reacquiring the signal. Furthermore, searching for a maximum signal to determine whether maximum antenna gain has been achieved will prove difficult for near-in high speed mobile units and signals experiencing fast fading. Signal level measurement uncertainty could also be construed as beam-width modulation further degrading accuracy.
Extensively used in many Radar and Sonar discriminators, another successful technique utilized for DOA tracking is known as mono-pulse beam forming and is described in “Introduction to Radar Systems”, M. I. Skolnik, 1980. Unlike ESPRIT and MUSIC techniques, mono-pulse estimation of DOA requires only the determination of a single ratio of two signals. The two signals are generated by forming two different beams from a single antenna: 1) a summation beam, 801, containing the signal information that is ultimately carried through the rest of the network and 2) a difference beam, 802. Over angle space this ratio is a well behaved function, 901 from which an accurate estimate of DOA relative to the current beam position may be determined. Its accuracy is an improvement over beam peak finding because of the sharpness of the difference beam null relative to the broad nature of the antenna beam. Its performance does rely on the ability to find the zero of the difference beam null and in a high interference or noisy environment this null tends to fill increasing the uncertainty of the angle offset estimate. Therefore, this technique requires a low interference environment. However, mobile communication lends itself to this technique due to the separation of radio links via various multi-access schemes such as CDMA TDMA, and FDMA. Thus, low interference is achieved through the orthogonality of the co-channel users.
Historically, mono-pulse tracking, although simple to implement, has not been utilized in multiple access communication systems. Digital beam-forming has only recently started to make a presence in practical systems due to the growth in processing speeds. Prior to digital implementations beam-forming systems have typically been realized in analog. To realize multiple beams in multiple access systems would require separate analog channels in the antenna beam-formers, including separate phase shifters and attenuators. The number of phase shifters and attenuators could number in the hundreds and even thousands per antenna, depending on the capacity of the system and the number of antenna elements in the phased array. This limits the number of simultaneous multiple beams to tens-of-beams and not hundreds-of-beams required for multi-access communication. Mono-pulse tracking has not been previously implemented for this application because it implies the real-time tracking of multiple simultaneous beams. Digital processors and ASIC's have just recently surpassed the performance requirements to achieve such a result. However, computational resources are still and always will be considered premium. Thus, a need exists to preserve as much of the computational resources as possible while enabling a significant increase in the capacity and range of communication systems.